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Insert image in R rmarkdown

![Caption for the Picture](path to the image file)

Sample Image

ggplot

library(ggplot2)
library(data.table)
dt <- data.table(var_x = 1:10, var_y= (1:10) ^ 2)
value_rho = as.character(0.77)
text <- expression("Spearman's" ~ rho ~ "= 0.77")
text <- bquote(atop("Top Line", "Spearman's" ~ rho ~ "=" ~ .(value_rho)))
ggplot(data = dt, mapping = aes(x = var_x, y = var_y)) +
    geom_point(size = 2, alpha = (1:10) / 10) +
    annotate("text", x = 2, y = 80, label = text)

library(data.table)
library(ggplot2)
dt <- data.table(A=1:10
               , B=1:10
               , C=rep(1:5, each=2)
               , sex=factor(rep(c("M", "F"), each=5))
                 )

clrs <- c("#FF0000", "#00FF00")
dt[["varcolor"]] <- clrs[as.numeric(dt$sex)]

plt <- function(x, y, group){
    x <- sym(x)
    y <- sym(y)
    ggplot(dt, aes(x=!!x, y=!!y)) +
        geom_point() +
        facet_wrap(as.formula(paste0("~", group)), nrow = 1)
}

plt("A", "B", "sex")

ggplot(dt, aes(A, B, colour=varcolor)) + geom_point() +
    scale_colour_identity()

ggplot(dt, aes(x=C, group=sex, colour=varcolor, fill=varcolor)) +
    geom_bar() + scale_colour_identity() + scale_fill_identity()

library(data.table)
dt <- data.table(
    day = sample(1:14, size = 1400, replace = TRUE)
  , state = sample(c("Good", "Bad"), size = 1400, replace = TRUE)
)

p <- ggplot(dt, aes(day)) +
    geom_bar() +
    geom_bar(aes(fill = state))
p

library(data.table)
dt <- data.table(
    day = sample(1:14, size = 1400, replace = TRUE)
  , state = sample(c("Good", "Bad"), size = 1400, replace = TRUE)
  , group = sample(c("I", "II", "III"), size = 1400, replace = TRUE)
)

p <- ggplot(dt, aes(x = day, fill = state)) +
    geom_bar(position = "fill") +
    facet_wrap( ~ group, ncol = 3)
p

library(data.table)
library(ggplot2)

set.seed(123456)
dt <- data.table(
    group = rep(c("A", "B", "C"), each = 300)
  , sex = sample(c("F", "M"), size = 900, replace = TRUE)
  , value = rnorm(900)
)

ggplot(dt, aes(y = value, x = group)) +
  geom_violin(trim=TRUE) +
  geom_boxplot(width=.1) +
  xlab("Group") + ylab("Value")

font_tick_size <- 10
font_axis_size_x <- 20
font_axis_size <- 15

ggplot(dt, aes(y = value, x=sex)) +
    geom_violin(trim=TRUE) +
    geom_boxplot(width=0.1) +
  facet_grid(cols=vars(group)) +
  ## scale_y_continuous(trans="log", breaks=brks) +
  labs(y = "Value", x = "Sex") +
        ## scale_y_continuous(breaks = seq(0,4,1), limits = c(-0.2,4), expand = c(0, 0)) +
        ## scale_x_continuous(breaks = seq(0,200,50), limits = c(-10,220), expand = c(0, 0)) +
  theme(axis.text.x = element_text(size = font_tick_size)
      , axis.text.y = element_text(size = font_tick_size)
      , axis.title.x = element_text(size = font_axis_size_x)
      , axis.title.y = element_text(size = font_axis_size)
     ## ,  plot.margin = unit(plot_margin, "cm")
       , axis.line = element_line(colour = "grey")
         ## , axis.ticks = element_blank()
         ## , panel.grid.major = element_line(colour = "#DDDDDD", size = 0.1)
           ## , panel.grid.minor = element_blank()
      , panel.background = element_blank()
      , strip.background =element_rect(fill="#FFFFFF")
        , strip.text.x = element_text(size = font_axis_size)
        )

## p <- ggplot(dt, aes(y = relative, x=sex)) +
##     geom_violin(trim=TRUE) +
##     facet_grid(rows=vars(area), cols=vars(age_group))

## p <- ggplot(dt, aes(y = relative, x=sex)) +
##     geom_violin(trim=TRUE) +
##     scale_y_continuous(trans="log", breaks=c(0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.64, 1.28, 2.56)) +
##     facet_grid(rows=vars(area), cols=vars(age_group)) +
##     labs(y="log transformed relative")

dt <- data.table(
    name=factor(LETTERS[1:8], levels=LETTERS[1:8], ordered=TRUE)
  , coef_value=1:8
  , coef_value_lower=(1:8) - 0.5
  , coef_value_upper=(1:8) + 0.5
)

dt <- dt[, name_label := factor(paste0(name, "  ", "CI"))]
dt <- dt[, num_rep := 22 - nchar(round(coef_value, 4)) ]



p <- ggplot(dt
          , aes(y = name_label
              , x = coef_value
              , xmin = coef_value_lower
              , xmax = coef_value_upper
                )) +
    geom_vline(xintercept=1, color='grey', linetype='dashed',size=0.7) +
    geom_errorbarh(height=0.2,color="#333333",size=0.8) +
    geom_point(color = "#666666", size=2, shape=15) +
    scale_x_continuous(limits=c(0,9)
                     , breaks=c(0,1,2,5, 9)
                     , name='Odds Ratio') +
    ## p <- p + scale_y_discrete(labels = "Y AXIS", sec.axis = dup.axis())
    ylab("") +
    theme_bw() +
    theme(axis.ticks = element_blank()
        , panel.grid.major = element_blank()
        , panel.grid.minor = element_blank()
          ) +
    coord_cartesian(xlim=c(0, 9))
p

## png(file="forestplot.png", width = 2400, height = 1200, res = 300)
## p
## dev.off()

png(file = "filname.png", width = 2400, height = 1200, res = 300)

p
dev.off()

png(file="forestplot_irf.png", width = 2400, height = 1200, res = 300)
p <- ggplot(dt
          , aes(y = name_label
              , x = coef_value
              , xmin = coef_value_lower
              , xmax = coef_value_upper
                ))
p <- p + geom_vline(xintercept=1, color='grey', linetype='dashed',size=0.7)
p <- p + geom_errorbarh(height=0.2,color="#333333",size=0.8)
p <- p + geom_point(color = "#666666",size=1,shape=15)

p <- p + scale_x_continuous(limits=c(0.1,4), breaks=c(0.1, 0.2, 0.5,1,2,3, 4), name='Adjusted Odds Ratio (IPR)')
## p <- p + scale_y_discrete(labels = "Y AXIS", sec.axis = dup.axis())
p <- p + ylab("")
p <- p + theme_bw()
p <- p + theme(axis.ticks = element_blank()
              , panel.grid.major = element_blank(),
               panel.grid.minor = element_blank()
               )
p <- p + coord_cartesian(xlim=c(0, 4)) +  coord_trans(x="log")
p
dev.off()


include_graphics("forestplot_irf.png")

library(ggplot2)

dt <- data.table(
    id=1:10
  , day=rep(1:28, each=10)
  , state=sample(c("A", "B", "C", "D", "E", NA), 28 * 10, replace = TRUE)
)

dt <- dt[, state := factor(state, levels=c("A", "B", "C", "D", "E"), ordered = TRUE)]


clrs <- c("#2EAEE6"
        ## , "#2EE6CA"
        ## , "#2EE677"
        , "#37E62E"
        ## , "#8AE62E"
        , "#DCE62E"
        , "#E69C2E"
        , "#E6492E"
          ## , "#aaaaaa"
          )


ggplot(dt, aes(x=day, y=id)) +
    geom_tile(aes(fill=state, height=(1)), size=1) +
    scale_y_discrete() +
    scale_fill_manual(values = clrs)

linecolor <- "#999999"

ggplot(dt, aes(y=id,x=day)) +
    geom_point(aes(fill=state), colour="transparent", shape=22, size=4) +
    scale_fill_manual(values = clrs)+
    geom_line(aes(group = id), colour=linecolor, alpha=0.5) +
    scale_x_continuous(breaks=c(7, 14, 21, 28)) +
    scale_y_continuous(breaks=c(1, 3, 5, 7, 9)) +
    labs(title = "Title. \n Discrete Time Plot", x="Day", y="ID") +
    geom_vline(xintercept=c(7, 14), colour=linecolor,linetype="dashed") +
    geom_vline(xintercept=21,colour=linecolor,linetype="dashed") +
    theme(panel.background = element_rect(fill = "transparent")
        , plot.background = element_rect(fill = "transparent", color = NA)
        , panel.grid.major = element_blank()
        , panel.grid.minor = element_blank()
        , legend.background = element_rect(fill = "transparent")
        , legend.box.background = element_rect(fill = "transparent")
        , legend.position = 'top'
        , plot.margin=unit(c(1,2,1,2),"cm")
          ) +
    coord_fixed(ratio = 3 / 2
              , xlim = c(0.5, 28.5)
              , ylim = c(0.5, 10.5)
              , expand = FALSE) +
    guides(fill = guide_legend(nrow = 1))

rm(list=ls())
library(ggplot2)
library(data.table)
dt <- data.table(
    id=rep(1:100, each=28)
  , day=rep(1:28, 100)
  , state=sample(c("A", "B", "C", "D", "E"), 28 * 100, replace = TRUE)
    , treatment=rep(c("Case", "Control"), each=28 * 100 / 2)
)

dt <- dt[, state := factor(state, levels=c("A", "B", "C", "D", "E"), ordered = TRUE)]


clrs <- c("#2EAEE6"
        ## , "#2EE6CA"
        ## , "#2EE677"
        , "#37E62E"
        ## , "#8AE62E"
        , "#DCE62E"
        , "#E69C2E"
        , "#E6492E"
          ## , "#aaaaaa"
          )


ggplot(dt, aes(x = factor(day), fill = state)) + geom_bar(position = "fill") +
    facet_wrap(~treatment) + scale_fill_manual(values = clrs) +
    xlab("Day State by Treatment") +
    ylab("Proportions")

ggplot(dt, aes(x=day, fill = state)) + geom_bar() +
    facet_wrap(~treatment) + scale_fill_manual(values = clrs) +
    labs(x="Day State by Treatment"
       , y="Count"
       , title="Progression by Treatment"
         ) +
    scale_x_continuous(breaks=c(7, 14, 21, 28)) +
    scale_y_continuous(breaks=c(10, 20, 30, 40, 50)) +
    theme(panel.background = element_rect(fill = "transparent")
        , plot.background = element_rect(fill = "transparent", color = NA)
        , panel.grid.major = element_blank()
        , panel.grid.minor = element_blank()
        , legend.background = element_rect(fill = "transparent")
        , legend.box.background = element_rect(fill = "transparent")
        , legend.position = 'top'
        , plot.margin=unit(c(1,2,1,2),"cm")
          ) +
    coord_fixed(ratio = 3 / 2
              ## , xlim = c(0.5, 28.5)
              ## , ylim = c(0.5, 10.5)
              , expand = FALSE) +
    guides(fill = guide_legend(nrow = 1))

ggplot(dt, aes(x = factor(treatment), fill = state)) + geom_bar() +
    facet_wrap(~factor(day), nrow=1) + scale_fill_manual(values = clrs) +
    labs(x="Day State by Treatment"
       , y="Count"
       , title="Progression by Day"
         ) +
    ## scale_x_continuous(breaks=c(7, 14, 21, 28)) +
    ## scale_y_continuous(breaks=c(10, 20, 30, 40, 50)) +
    theme(panel.background = element_rect(fill = "transparent")
        , plot.background = element_rect(fill = "transparent", color = NA)
        , panel.grid.major = element_blank()
        , panel.grid.minor = element_blank()
        , legend.background = element_rect(fill = "transparent")
        , legend.box.background = element_rect(fill = "transparent")
        , legend.position = 'top'
        , plot.margin=unit(c(1,2,1,2),"cm")
          ) +
    ## coord_fixed(ratio = 3 / 2
              ## , xlim = c(0.5, 28.5)
              ## , ylim = c(0.5, 10.5)
              ## , expand = FALSE) +
    guides(fill = guide_legend(nrow = 1))

ggplot(dt, aes(status_day5_num)) + stat_ecdf(geom = "step") +
    scale_x_continuous(limits=c(0.8, 8.8)
                       , breaks=1:8
                       , labels=levels(dt$status_day5)
                     , name="Numerical Day 5 Status")

ggplot(dt, aes(x=avg_daily_prone, fill=treatment)) +
    geom_histogram( color="#e9ecef", alpha=0.3, position = 'identity') +
    scale_fill_manual(values=c("#69b3a2", "#404080"))

ROC Curve

library(data.table)
set.seed(123456)
n <- 1000
dt <- data.table(x = runif(n))
p0 <- 0.2
or <- 2
dt <- dt[, odds0 := p0 / (1 - p0)
         ][, log_odds := log(odds0) + x * log(or)
           ][, p := exp(log_odds) / (1 + exp(log_odds))]

hist(dt$p)

vsample <- function(p){
    sample(c(1, 0), size = 1, replace = TRUE, prob = c(p, 1 - p))
}
vsample <- Vectorize(vsample)
dt <- dt[, outcome := vsample(p)]

m <- glm(outcome ~ x, data = dt, family = binomial)


library(sjPlot)
tab_model(m)

pred <- predict(m, newdata = dt, type = "response")
library(pROC)
r <- roc(dt$outcome, pred, ci = TRUE, direction = "<")


dr <- data.table(
    tpr = r$sensitivities
  , fpr = 1 - r$specificities
)
dr <- dr[order(fpr, tpr)]

ggplot(dr, aes(x = fpr, y = tpr)) +
    geom_segment(x = 0, y = 0, xend = 1, yend = 1, size = 0.5, color = "#999999", linetype = "longdash") + 
    geom_step(size = 1.5, color = "#333333", direction = "hv") +
    xlab("FPR") + ylab("TPR") + 
    theme_bw() +
    theme(axis.ticks.x = element_blank()
        , axis.ticks.y = element_blank()
        , axis.text.x = element_blank()
        , axis.text.y = element_blank()
        , panel.grid.major = element_blank()
        , panel.grid.minor = element_blank()
          )

r <- roc(dt$outcome, pred, ci = TRUE, direction = "<")

plt_roc(r) %>% ann("ROC Curve")

plt_roc2(r1, r2)
tst <- roc.test(r1, r2, method = "delong")

t <- data.table(
  test = tst$alternative
  , compare = attr(tst$null.value, "names")
  , method = tst$method
  , pvalue = tst$p.value
)

Color

library(ggplot2)

dt <- expand.grid(seq(0, 340, 20), seq(0, 100, 10), seq(0, 100, 10))
dt <- as.data.table(dt)
colnames(dt) <- c("Hue", "Saturation", "Luminance")
## dt <- dt[, clr := hcl(Hue, Chroma, Luminance)]
dt <- dt[, clr := hsv(Hue / 360, Saturation / 100, Luminance / 100)]
ggplot(dt, aes(x=Saturation, y=Luminance, colour=clr)) +
    geom_point(size=3) +
    scale_color_identity() +
    scale_x_continuous(breaks=seq(0, 100, 10)) +
    scale_y_continuous(breaks=seq(0, 100, 10)) +
    facet_wrap( ~ Hue, nrow=6)

plotly

dt <- readRDS(file="df.RDS")

rl <- dt[, .(m_mmdhp=median(sqrt(mmdhp_score_imp), na.rm = TRUE)
           , m_js=median(edmcq_js_score_imp, na.rm = TRUE)
           , ms_clt=median(edmcq_js_score_imp, na.rm = TRUE)
           , ms_ldr=median(edmcq_ldr_score_imp, na.rm = TRUE)
           , m_eol=median(edmcq_eol_score_imp, na.rm = TRUE)
             )
       , by = list(gender_q3.factor)
         ][order(gender_q3.factor)]

rl$m_mmdhp2 <- rl$m_mmdhp


dms <- c("MMD-HP", "Job Strain"
       , "Safety Culture"
       , "Leadership Culture"
       , "End of Life"
       , "MMD-HP"
         )

opc <- 0.5
fig <- plot_ly(
    type = 'scatterpolar',
    fill = 'toself'
  )
fig <- fig %>%
  add_trace(
    r = t(rl[1, 2:7, drop = TRUE])
    , theta = dms
  , name = rl$gender_q3.factor[1]
  , opacity = opc
  )

fig <- fig %>%
  add_trace(
    r = t(rl[2, 2:7, drop = TRUE])
  , theta = dms
  , name = rl$gender_q3.factor[2]
  , opacity = opc
  )
fig <- fig %>%
  layout(
    polar = list(
      radialaxis = list(
        visible = T,
        range = c(0, 4)
      )
    )
  )

fig

library(plotly)
p1 <- dt[mg_tp0.factor == "Yes"]$pred
p0 <- dt[mg_tp0.factor == "No"]$pred

plot_ly(alpha = 0.5, xbins = list(start = 0, end = 1, size = 0.02)) %>%
  add_histogram(x = ~ p1
              , name = "Magnesium"
              , inherit = TRUE
              ## , xbins = seq(0, 1, 0.05)
                ) %>%
  add_histogram(x = ~ p0
              , name = "No"
              , inherit = TRUE
              ## , xbins = seq(0, 1, 0.05)
                ) %>%
    layout(barmode = "overlay"
         , xaxis = list(title = paste0("Predicted Probabilities of Being Treated with Magnesium (AUC = ",format(r$auc,digits = 3), ")"),
                      zeroline = FALSE),
         yaxis = list(title = "Count",
                      zeroline = FALSE))

Animation

library(gifski)
png("frame%03d.png")
par(ask = FALSE)
for(i in 1:10)
  plot((1:10) ^ (sqrt(i)), main = i)
dev.off()

png_files <- sprintf("frame%03d.png", 1:10)
gif_file <- tempfile(fileext = ".gif")
gifski(png_files, gif_file)

## utils::browseURL(gif_file)
knitr::include_graphics(gif_file)

# Example borrowed from gganimate
library(gapminder)
library(ggplot2)
makeplot <- function(){
  datalist <- split(gapminder, gapminder$year)
  lapply(datalist, function(data){
    p <- ggplot(data, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
      scale_size("population", limits = range(gapminder$pop)) + geom_point() + ylim(20, 90) +
      scale_x_log10(limits = range(gapminder$gdpPercap)) + ggtitle(data$year) + theme_classic()
    print(p)
  })
}

# High Definition images:
gif_file <- save_gif(makeplot(), width = 800, height = 450, res = 92)
## utils::browseURL(gif_file)

knitr::include_graphics(gif_file)

library(ggplot2)
library(gganimate)

ggplot(mtcars, aes(factor(cyl), mpg)) +
  geom_boxplot() +
  # Here comes the gganimate code
  transition_states(
    gear,
    transition_length = 2,
    state_length = 1
  ) +
  enter_fade() +
  exit_shrink() +
  ease_aes('sine-in-out')

library(gapminder)

ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  facet_wrap(~continent) +
  # Here comes the gganimate specific bits
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')

R sessionInfo

sessionInfo()

R version 4.2.0 (2022-04-22) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 20.04.3 LTS

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale: [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C

attached base packages: [1] stats graphics grDevices utils datasets methods base

other attached packages: [1] gapminder_0.3.0 gifski_1.6.6-1 pROC_1.18.0
[4] sjPlot_2.8.10 Wu_0.0.0.9000 flexdashboard_0.5.2 [7] lme4_1.1-29 Matrix_1.4-0 mgcv_1.8-38
[10] nlme_3.1-152 png_0.1-7 scales_1.2.0
[13] nnet_7.3-16 labelled_2.9.1 kableExtra_1.3.4
[16] plotly_4.10.0 gridExtra_2.3 ggplot2_3.3.6
[19] DT_0.23 tableone_0.13.2 magrittr_2.0.3
[22] lubridate_1.8.0 dplyr_1.0.9 plyr_1.8.7
[25] data.table_1.14.2 rmdformats_1.0.4 knitr_1.39

loaded via a namespace (and not attached): [1] insight_0.18.0 webshot_0.5.3 httr_1.4.3 backports_1.4.1
[5] tools_4.2.0 bslib_0.3.1 sjlabelled_1.2.0 utf8_1.2.2
[9] R6_2.5.1 DBI_1.1.2 lazyeval_0.2.2 colorspace_2.0-3 [13] withr_2.5.0 tidyselect_1.1.2 emmeans_1.7.5 compiler_4.2.0
[17] performance_0.9.1 cli_3.3.0 rvest_1.0.2 xml2_1.3.3
[21] sandwich_3.0-2 labeling_0.4.2 bookdown_0.27 bayestestR_0.12.1 [25] sass_0.4.1 mvtnorm_1.1-3 systemfonts_1.0.4 stringr_1.4.0
[29] digest_0.6.29 minqa_1.2.4 rmarkdown_2.14 svglite_2.1.0
[33] pkgconfig_2.0.3 htmltools_0.5.2 fastmap_1.1.0 highr_0.9
[37] htmlwidgets_1.5.4 rlang_1.0.2 rstudioapi_0.13 jquerylib_0.1.4
[41] generics_0.1.2 farver_2.1.0 zoo_1.8-10 jsonlite_1.8.0
[45] crosstalk_1.2.0 parameters_0.18.1 Rcpp_1.0.8.3 munsell_0.5.0
[49] fansi_1.0.3 lifecycle_1.0.1 multcomp_1.4-19 stringi_1.7.6
[53] yaml_2.3.5 MASS_7.3-54 grid_4.2.0 sjmisc_2.8.9
[57] forcats_0.5.1 crayon_1.5.1 lattice_0.20-45 ggeffects_1.1.2
[61] haven_2.5.0 splines_4.2.0 sjstats_0.18.1 hms_1.1.1
[65] pillar_1.7.0 boot_1.3-28 estimability_1.4 effectsize_0.7.0 [69] codetools_0.2-18 glue_1.6.2 evaluate_0.15 mitools_2.4
[73] modelr_0.1.8 vctrs_0.4.1 nloptr_2.0.3 gtable_0.3.0
[77] purrr_0.3.4 tidyr_1.2.0 datawizard_0.4.1 xfun_0.31
[81] broom_0.8.0 xtable_1.8-4 survey_4.1-1 survival_3.2-13
[85] viridisLite_0.4.0 tibble_3.1.7 TH.data_1.1-1 ellipsis_0.3.2